When analyzing data, one often wants to optimize simultaneously several characteristics. For example, a customer may want a smartphone with a battery life as long as possible, but as cheap as possible. Or a football team coach may want a player which is good both at scoring goals and at making passes. It is difficult to optimize simultaneously multiple and potentially conflicting criteria (i.e. dimensions), and several compromises can be acceptable. Skyline queries are a tool to discover and present such compromises. In a multidimensional space where the dimension domains are ordered, skyline queries return the objects which are not dominated by any other object. An object dominates another object, if it is as good or better in all dimensions and better in at least one dimension. For example, with the smartphone example several skyline choices may be the cheapest smartphone having a very long battery life, or the most endurant of the budget smartphones, as well as the smartphone being average on both criteria.

Several studies were carried out on skyline analysis [1] as a retrieval tool in a decisional context. Skyline queries can formulate multicriteria queries associated with preferences and obtain the top answers, for example to ﬁnd the best soccer players according to their technical performance and behaviours. In some applications, one may require the deﬁnition of constraints on some dimensions and/or objects to express hard restrictions. For example, find the best soccer players that hold a permanent french nationality to play for the representative french team. This issue was investigated in several works [2,3].

However, conventional skyline queries are not adapted to answer queries that require to analyze not only individual objects but also groups of objects. For example, find the best soccer teams of eleven players. Recent works [4,5,6,7] have considered the issue of group skyline computation, and enable the user to perform skyline queries on object groups in order to select the most relevant one. But, to the best of our knowledge, none of these works have investigated group skyline queries, when dealing with constraints. It is not clear how to answer the following queries using only conventional group skyline computation when we wish to form a team of eleven players:

How to find the best soccer teams with six defenders, four attackers and one goalkeeper ?

We know that the best players don't always make the best team. So, how to take into account the teammates' cooperation to build the best team ?

An interesting problem arises when users are allowed to deﬁne constraints over group skyline analysis. This problem is challenging when there are many objects in the dataset. Indeed, constraints reduce the input size, yet, paradoxically, makes computing the skyline quite challenging.

Another issue that must be taken into account is the problem of group skyline retrieval when the size of the requested group is not known in advance. The challenge here is to extract the smallest skyline groups that satisfy the constraints in an effective way.

This work aims to (1) propose eﬃcient Group-based Skyline methods that incorporate constraints into the search space and cope with dynamic constraints, (2) formally deﬁne a novel Constrained Group-based Skyline by extending the definition of the dominance relation, and (3) propose the use of the Minimum Description Length principle [8] to select the smallest groups of skyline that describe the data best.

For the evaluation of the proposed methods, we have a consequent agri-environnemental database of simulations (several Gbyte in size) to perform our experiments. For this dataset, we also have experts in the field (i.e. domain knowledge) to validate the skyline results. The goal for this use case is to identify the simulation groups (i.e. group skyline) in which the agronomic criteria variability is sufficient to cover all possible scenarios.

Furthermore, our team is co-developing, in the context of the Headwork ANR project, a crowdsourcing platform allowing the management of complex tasks and workflows. It would be interesting to integrate the proposed methods in this platform, allowing for effective selection of team of workers (i.e. group skyline) to perform a complex task that requires complementary skills in a variety of areas.